Cosmic ray composition study using machine learning at the IceCube Neutrino Observatory
The evaluation of mass composition of cosmic rays in the knee region ($\sim 3$ PeV) is critical to understanding the transition in the origin of cosmic rays from galactic to extragalactic sources. The IceCube Neutrino Observatory at the South Pole is a multi-component detector consisting of the surf...
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
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arXiv
2019
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| Online Access: | https://dx.doi.org/10.48550/arxiv.1908.06433 https://arxiv.org/abs/1908.06433 |
| Summary: | The evaluation of mass composition of cosmic rays in the knee region ($\sim 3$ PeV) is critical to understanding the transition in the origin of cosmic rays from galactic to extragalactic sources. The IceCube Neutrino Observatory at the South Pole is a multi-component detector consisting of the surface IceTop array and the deep in-ice IceCube detector. By applying modern machine-learning techniques to cosmic-ray air showers reconstructed coincidentally in both detector components of IceCube observatory, the energy and the mass of primary cosmic rays in this transition region can be measured. In this contribution, we will discuss the reconstruction performance and composition sensitivity of IceCube observables presently under development. : Presented at the 36th International Cosmic Ray Conference (ICRC 2019). See arXiv:1907.11699 for all IceCube contributions |
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